Ontology-based complexity management in conceptual modeling - Figueiredo, Guylerme Velasco de Souza (2022)
Reference conceptual models are used to capture complex and critical domain information. However, as the complexity of a domain grows, so does the size and complexity of the model that represents it. Over the years, different complexity management techniques in large-scale conceptual models have been developed to extract value from models that, due to their size, are challenging to understand. These techniques, however, run into some limitations, such as the possibility of execution without human interaction, semantic cohesion of modules/views generated from the model, and generating an abstracted version of the model so that it can present the essential elements of the domain, among others. This thesis proposes two algorithms to facilitate the understanding of large-scale conceptual models by tackling the problem from two different angles. The first consists in extracting smaller self-contained modules from the original model. The second consists in abstracting the original model, thereby providing a summarized view of the main elements and how they relate to each other in the domain. Both algorithms we propose in this thesis require no input from modelers, are deterministic, and computationally inexpensive. To evaluate the abstraction algorithm for conceptual models, we carried out an empirical research aimed at a comparative analysis taking into account other competing approaches.